Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks
Fig 2
Comparisons of different strategies used to train TripletRes.
(a-c) Comparisons of the average long-range top-L/5 precisions over training epochs using different feature extraction strategies but trained with the same deep neural-network structure on three different coevolutionary analysis methods: (a) DCA based on pseudolikelihood maximization (PLM), (b) DCA based on the precision matrix (PRE), (c) Covariance analysis (COV) for contact-map prediction, on the validation set. “Processed” means the coevolutionary features are post-processed by Eqs A and B in S4 Text. (d) Comparison of the average long-range top-L/5 precisions over training epochs of individual coevolutionary features and the TripletRes model that ensembles all three sets of features, on the validation set. Each curve is for the training of a single model. (e) Comparison of long-range top-L/5 and top-L precisions with different loss functions on the CASP FM and CAMEO hard targets.